class_docstr: Tests correctness of the power transformation.
code: def testBijector(self):
      c = 0.2
      bijector = power_transform.PowerTransform(
          power=c, validate_args=True
      )
      self.assertStartsWith(bijector.name, "power_transform")
      x = np.array([[[-1.0], [2.0], [-5.0 + 1e-4]]])
      y = (1.0 + x * c) ** (1.0 / c)
      self.assertAllClose(y, self.evaluate(bijector.forward(x)))
      self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
      self.assertAllClose(
          (c - 1.0) * np.sum(np.log(y), axis=-1),
          self.evaluate(
              bijector.inverse_log_det_jacobian(y, event_ndims=1)
          ),
      )
      self.assertAllClose(
          self.evaluate(
              -bijector.inverse_log_det_jacobian(y, event_ndims=1)
          ),
          self.evaluate(
              bijector.forward_log_det_jacobian(x, event_ndims=1)
          ),
          rtol=1e-4,
          atol=0.0,
      )
  
file_path: probability/tensorflow_probability/python/bijectors/power_transform_test.py
imports: import numpy
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import power_transform
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the PowerTransformBijectorTest obj's underlying member method code with example usage: obj.testBijector() without return types